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Application of Image Filtering Technique Based on Bounded Mean Oscillation Model in Studying on Rice Grain Morphology

文献类型： 外文期刊

作者： Hua, Shan 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Agr Equipment, Minist Agr & Rural Affairs, Key Lab Creat Agr, Hangzhou 310021, Peoples R China

关键词： Bounded mean oscillation; partial differential equation; image filtering; rice grain morphological characteristics

期刊名称：INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE （ 影响因子：1.373； 五年影响因子：1.346 ）

ISSN： 0218-0014

年卷期： 2021 年 35 卷 1 期

页码：

收录情况： SCI

摘要： This study constructed a bounded mean oscillation (BMO) filter via the BMO algorithm and anisotropic nonlinear partial differential equation (PDE) to both denoise and enhance the digital image of rice grains. The Perona-Malik PDE model was used as control filter. Based on the quantitative evaluation of the morphological characteristics of rice grains, as obtained from preprocessed images, the BMO filtering effect is discussed. The results showed that grain length, grain width, and the length-width ratio obtained from BMO filter processed images did not significantly differ from manual measurements (p>0.05). Moreover, a strong positive correlation was found between the average grain area and the thousand grain weight (R2=0.942, p<0.001). The BMO filter was less disturbed by noise and the structure of the utilized algorithm was simpler compared with the Perona-Malik filter. The developed BMO filter was also superior to the Perona-Malik filter in retaining fine edge features of digital images. Moreover, its filtering effect remained stable for grain images of different rice varieties.

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